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ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to...

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Autores principales: Wilson, Colorado, Lewis, Karen A., Fitzkee, Nicholas C., Hough, Loren E., Whitten, Steven T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464302/
https://www.ncbi.nlm.nih.gov/pubmed/37574757
http://dx.doi.org/10.1002/pro.4756
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author Wilson, Colorado
Lewis, Karen A.
Fitzkee, Nicholas C.
Hough, Loren E.
Whitten, Steven T.
author_facet Wilson, Colorado
Lewis, Karen A.
Fitzkee, Nicholas C.
Hough, Loren E.
Whitten, Steven T.
author_sort Wilson, Colorado
collection PubMed
description We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase‐separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences.
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spelling pubmed-104643022023-09-01 ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level Wilson, Colorado Lewis, Karen A. Fitzkee, Nicholas C. Hough, Loren E. Whitten, Steven T. Protein Sci Tools for Protein Science We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase‐separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences. John Wiley & Sons, Inc. 2023-09-01 /pmc/articles/PMC10464302/ /pubmed/37574757 http://dx.doi.org/10.1002/pro.4756 Text en © 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Tools for Protein Science
Wilson, Colorado
Lewis, Karen A.
Fitzkee, Nicholas C.
Hough, Loren E.
Whitten, Steven T.
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title_full ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title_fullStr ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title_full_unstemmed ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title_short ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
title_sort parse 2.0: a web tool to identify drivers of protein phase separation at the proteome level
topic Tools for Protein Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464302/
https://www.ncbi.nlm.nih.gov/pubmed/37574757
http://dx.doi.org/10.1002/pro.4756
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